Module Catalogues

Artificial Intelligence

Module Title Artificial Intelligence
Module Level Level 1
Module Credits 5.00
Academic Year 2021/22
Semester SEM2

Aims and Fit of Module

This module gives an introduction to Artificial Intelligence (AI) by (1) demonstrating the knowledge of major AI techniques; (2) providing a ground in AI algorithm implementation, which is used as a vehicle for practical illustrations; (3) understanding the framework of an AI system; and (4) introducing students the advances of AI techniques including computer vision, natural language processing and machine listening. The specific aims are: (1) to introduce students the concept of AI techniques, AI systems and related theories; (2) to introduce students the intelligent systems built on the use of AI techniques; (3) to provide a grounding in algorithm implementation; (4) to introduce students some of the modern AI developments including computer vision, natural language processing and machine listening.

Learning outcomes

A. Demonstrate knowledge of the basic principles of artificial intelligence. B. Gain experience in the related AI area such as experiment design and result analysis. C. Become familiar with the major AI paradigms, e.g., rule based and statistical learning based methods. D. Apply the knowledge of how AI theories can be manipulated to solve problems in an intelligent system context. E. Acquire the essentials of AI algorithm implementation so as to enable further exploration of the usage of AI. F. Acquire the fundamental knowledge of modern AI concepts and technologies including computer vision, natural language processing and machine listening.

Method of teaching and learning

Students will be expected to attend two to three hours of formal lectures in a typical week. Lectures will introduce students to the academic content and practical skills which are the subject of the module. Computer labs are arranged in three sessions where each session lasts four hours. The computer practical allows students to use those tolls and practice the acquired techniques. In addition, students will be expected to devote six to seven hours of unsupervised time to solving continuous assessment tasks and private study. Private study will Private study will provide time for reflection and consideration of lecture material and background reading. Continuous assessment will be used to test to what extent practical skills have been learned, in particular, assessment tasks will be solved individually and each solution comprises the resolution, using sound software engineering techniques, of the given problems expressed in terms of a requirements statement.